SoroushVahidi/scidocs huggingface dataset:
Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals
(1999)

In this study, we investigated blood-flow-related magnetic-resonance (MR) signal changes and the time course underlying short-term motor learning of the dominant right hand in ten piano players (PPs) and 23 non-musicians (NMs), using a complex finger-tapping task. The activation patterns were analyzed for selected regions of interest (ROIs) within the two examined groups and were related to the subjects’ performance. A functional learning profile, based on the regional blood-oxygenation-level-dependent (BOLD) signal changes, was assessed in both groups. All subjects achieved significant increases in tapping frequency during the training session of 35 min in the scanner. PPs, however, performed significantly better than NMs and showed increasing activation in the contralateral primary motor cortex throughout motor learning in the scanner. At the same time, involvement of secondary motor areas, such as bilateral supplementary motor area, premotor, and cerebellar areas, diminished relative to the NMs throughout the training session. Extended activation of primary and secondary motor areas in the initial training stage (7–14 min) and rapid attenuation were the main functional patterns underlying short-term learning in the NM group; attenuation was particularly marked in the primary motor cortices as compared with the PPs. When tapping of the rehearsed sequence was performed with the left hand, transfer effects of motor learning were evident in both groups. Involvement of all relevant motor components was smaller than after initial training with the right hand. Ipsilateral premotor and primary motor contributions, however, showed slight increases of activation, indicating that dominant cortices influence complex sequence learning of the non-dominant hand. In summary, the involvement of primary and secondary motor cortices in motor learning is dependent on experience. Interhemispheric transfer effects are present.
Analysis of political discourse on twitter in the context of the 2016 US presidential elections
(2017)

Social media now plays a pivotal role in electoral campaigns. Rapid dissemination of information through platforms such as Twitter has enabled politicians to broadcast their message to a wide audience. In this paper, we investigated the sentiment of tweets by the two main presidential candidates, Hillary Clinton and Donald Trump, along with almost 2.9 million tweets by Twitter users during the 2016 US Presidential Elections. We analyzed these short texts to evaluate how accurately Twitter represented the public opinion and real world events of significance related with the elections. We also analyzed the behavior of over a million distinct Twitter users to identify whether the platform was used to share original opinions and to interact with other users or whether few opinions were repeated over and over again with little inter-user dialogue. Finally, we wanted to assess the sentiment of tweets by both candidates and their impact on the election related discourse on Twitter. Some of our findings included the discovery that little original content was created by users and Twitter was primarily used for rebroadcasting already present opinions in the form of retweets with little communication between users. Also of significance was the finding that sentiment and topics expressed on Twitter can be a good proxy of public opinion and important election related events. Moreover, we found that Donald Trump offered a more optimistic and positive campaign message than Hillary Clinton and enjoyed better sentiment when mentioned in messages by Twitter users.
Warpage control of silicon interposer for 2.5D package application
(2013)

In order to achieve high speed transmission and large volume data processing, large size silicon-interposer has been required. Warpage caused by the CTE mismatch between a large silicon-interposer and an organic substrate is the most significant problem. In this study, we investigated several warpage control techniques for 2.5D package assembly process. First was assembly process sequence. One is called “chip first process” that is, chips are mounted on Si-interposer at first. The other is called “chip last process” that is, silicon-interposer is mounted on organic substrate at first and chips are mounted on at last. The chip first process successfully processed using conventional mass reflow. By using the chip first process, apparent CTE of a large silicon-interposer become close to that of an organic substrate. Second was the warpage control using underfill resin. We focused on the selection of underfill materials for 0 level assembly. And third was the warpage control technique with Sn-57Bi solder using conventional reflow process. We observed warpage change during simulated reflow process using three-dimensional digital image correlation system (3D-DIC). Sn-57Bi solder joining has been noted as a low temperature bonding methods. It is possible to lower peak temperature 45-90 degree C during reflow compared with using Sn3.0wt%Ag0.5wt%Cu (SAC305). By using Sn-57Bi solder, the warpage after reflow was reduced to 75% of that using SAC305. The full assembly was successfully processed using conventional assembly equipment and processes. The full assembly packages were evaluated by some reliability tests. All samples passed each reliability test.
KCT: a MATLAB toolbox for motion control of KUKA robot manipulators
(2010)

The Kuka Control Toolbox (KCT) is a collection of MATLAB functions for motion control of KUKA robot manipulators, developed to offer an intuitive and high-level programming interface to the user. The toolbox, which is compatible with all 6 DOF small and low payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 30 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display and diagnostics. The flexibility, ease of use and reliability of the toolbox is demonstrated through two applicative examples.
Using Data Mining Techniques for Sentiment Shifter Identification
(2016)

Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent.
Computing the Stereo Matching Cost with a Convolutional Neural Network Seminar Recent Trends in 3 D Computer Vision
(2015)

This paper presents a novel approach to the problem of computing the matching-cost for stereo vision. The approach is based upon a Convolutional Neural Network that is used to compute the similarity of input patches from stereo image pairs. In combination with state-ofthe-art stereo pipeline steps, the method achieves top results in major stereo benchmarks. The paper introduces the problem of stereo matching, discusses the proposed method and shows results from recent stereo datasets.
Evolution of fashion brands on Twitter and Instagram
(2015)

Social media has become a popular platform for marketing and brand advertisement especially for fashion brands. To promote their products and gain popularity, different brands post their latest products and updates as photos on different social networks. Little has been explored on how these fashion brands use different social media to reach out to their customers and obtain feedback on different products. Understanding this can help future consumers to focus on their interested brands on specific social media for better information and also can help the marketing staff of different brands to understand how the other brands are utilizing the social media. In this article we focus on the top-20 fashion brands and comparatively analyze how they target their current and potential future customers on Twitter and Instagram. Using both linguistic and deep image features, our work reveals an increasing diversification of trends accompanied by a simultaneous concentration towards a few selected trends. It provides insights about brand marketing strategies and their respective competencies. Our investigations show that the brands are using Twitter and Instagram in a distinctive manner.
Organizational Learning and Communities of Practice ; A social constructivist perspective
(2002)

In this paper, the relationship between organizational learning (OL) and communities of practice (COP) is addressed. A social constructivist lens is used to analyzing the potential contributions of COP’s in supporting learning by organizations. A social constructivist approach sees organizational learning as an institutionalizing process. The attention is on the process through which individual or local knowledge is transformed into collective knowledge as well as the process through which this socially constructed knowledge influences, and is part of, local knowledge. In order to analyse COP’s contribution to OL, we use the three phases or ‘moments’ described by Berger and Luckman (1966) that can be discerned during institutionalizing knowledge: ‘externalizing, objectifying and internalizing’. Externalizing knowledge refers to the process through which personal knowledge is exchanged with others. Objectifying knowledge refers to the process through which knowledge becomes an objective reality. Internalizing knowledge refers to the process through which objectified knowledge is used by individuals in the course of their socialization. In relation to OL processes, learning can be analyzed as consisting of these three knowledge sharing activities: externalizing individual knowledge resulting in shared knowledge; objectifying shared knowledge resulting in organizational knowledge; internalizing organizational knowledge resulting in individual knowledge. These various processes that in combination make up OL processes, are visualized by the use of a OL cycle. The cycle provides a simplified picture of OL seen as a process of institutionalization. The cycle is subsequently used to analyze the possible contribution of COP’s to support organizational learning. The paper concludes that COP’s are well suited to support processes of internalization and externalization. As a result, COP’s stimulate social learning processes within organizations. However, COP’s do not seem to be the appropriate means to support the process of objectification. This means that COP’s contribution in supporting learning at the organizational level or ‘organizational learning’ is much more complicated.
The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
(2015)

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems. We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost. In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development. We use the term “Dataflow Model” to describe the processing model of Google Cloud Dataflow [20], which is based upon technology from FlumeJava [12] and MillWheel [2]. This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles from this volume were invited to present their results at the 41st International Conference on Very Large Data Bases, August 31st September 4th 2015, Kohala Coast, Hawaii. Proceedings of the VLDB Endowment, Vol. 8, No. 12 Copyright 2015 VLDB Endowment 2150-8097/15/08.
A Trusted Safety Verifier for Process Controller Code
(2014)

Attackers can leverage security vulnerabilities in control systems to make physical processes behave unsafely. Currently, the safe behavior of a control system relies on a Trusted Computing Base (TCB) of commodity machines, firewalls, networks, and embedded systems. These large TCBs, often containing known vulnerabilities, expose many attack vectors which can impact process safety. In this paper, we present the Trusted Safety Verifier (TSV), a minimal TCB for the verification of safety-critical code executed on programmable controllers. No controller code is allowed to be executed before it passes physical safety checks by TSV. If a safety violation is found, TSV provides a demonstrative test case to system operators. TSV works by first translating assembly-level controller code into an intermediate language, ILIL. ILIL allows us to check code containing more instructions and features than previous controller code safety verification techniques. TSV efficiently mixes symbolic execution and model checking by transforming an ILIL program into a novel temporal execution graph that lumps together safetyequivalent controller states. We implemented TSV on a Raspberry Pi computer as a bump-in-the-wire that intercepts all controllerbound code. Our evaluation shows that it can test a variety of programs for common safety properties in an average of less than three minutes, and under six minutes in the worst case—a small one-time addition to the process engineering life cycle.
Discriminative Decorrelation for Clustering and Classification
(2012)

Object detection has over the past few years converged on using linear SVMs over HOG features. Training linear SVMs however is quite expensive, and can become intractable as the number of categories increase. In this work we revisit a much older technique, viz. Linear Discriminant Analysis, and show that LDA models can be trained almost trivially, and with little or no loss in performance. The covariance matrices we estimate capture properties of natural images. Whitening HOG features with these covariances thus removes naturally occuring correlations between the HOG features. We show that these whitened features (which we call WHO) are considerably better than the original HOG features for computing similarities, and prove their usefulness in clustering. Finally, we use our findings to produce an object detection system that is competitive on PASCAL VOC 2007 while being considerably easier to train and test.
Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs
(2003)

Although popular and extremely well established in mainstream statistical data analysis, logistic regression is strangely absent in the field of data mining. There are two possible explanations of this phenomenon. First, there might be an assumption that any tool which can only produce linear classification boundaries is likely to be trumped by more modern nonlinear tools. Second, there is a legitimate fear that logistic regression cannot practically scale up to the massive dataset sizes to which modern data mining tools are This paper consists of an empirical examination of the first assumption, and surveys, implements and compares techniques by which logistic regression can be scaled to data with millions of attributes and records. Our results, on a large life sciences dataset, indicate that logistic regression can perform surprisingly well, both statistically and computationally, when compared with an array of more recent classification algorithms.
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
(2002)

We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random elds (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modi cation of the proof of convergence of the perceptron algorithm for classi cation problems. We give experimental results on part-of-speech tagging and base noun phrase chunking, in both cases showing improvements over results for a maximum-entropy tagger.
Gratitude and depressive symptoms: the role of positive reframing and positive emotion.
(2012)

Eight studies (N=2,973) tested the theory that gratitude is related to fewer depressive symptoms through positive reframing and positive emotion. Study 1 found a direct path between gratitude and depressive symptoms. Studies 2-5 demonstrated that positive reframing mediated the relationship between gratitude and depressive symptoms. Studies 6-7 showed that positive emotion mediated the relationship between gratitude and depressive symptoms. Study 8 found that positive reframing and positive emotion simultaneously mediated the relationship between gratitude and depressive symptoms. In sum, these eight studies demonstrate that gratitude is related to fewer depressive symptoms, with positive reframing and positive emotion serving as mechanisms that account for this relationship.
A hybrid SoC interconnect with dynamic TDMA-based transaction-less buses and on-chip networks
(2006)

The two dominant architectural choices for implementing efficient communication fabrics for SoC's have been transaction-based buses and packet-based networks-on-chip (NoC). Both implementations have some inherent disadvantages - the former resulting from poor scalability and the transactional character of their operation, and the latter from inconsistent access times and deterioration of performance at high injection rates. In this paper, we propose a transaction-less, time-division-based bus architecture, which dynamically allocates timeslots on-the-fly - the dTDMA bus. This architecture addresses the contention issues of current bus architectures, while avoiding the multi-hop overhead of NoC's. It is compared to traditional bus architectures and NoC's and shown to outperform both for configurations with fewer than 10 PE's. In order to exploit the advantages of the dTDMA bus for smaller configurations, and the scalability of NoC's, we propose a new hybrid SoC interconnect combining the two, showing significant improvement in both latency and power consumption.
Information Extraction: Techniques and Challenges
(1997)

This volume takes a broad view of information extraction as any method for ltering information from large volumes of text. This includes the retrieval of documents from collections and the tagging of particular terms in text. In this paper we shall use a narrower de nition: the identi cation of instances of a particular class of events or relationships in a natural language text, and the extraction of the relevant arguments of the event or relationship. Information extraction therefore involves the creation of a structured representation (such as a data base) of selected information drawn from the text. The idea of reducing the information in a document to a tabular structure is not new. Its feasibility for sublanguage texts was suggested by Zellig Harris in the 1950's, and an early implementation for medical texts was done at New York University by Naomi Sager[20]. However, the speci c notion of information extraction described here has received wide currency over the last decade through the series of Message Understanding Conferences [1, 2, 3, 4, 14]. We shall discuss these Conferences in more detail a bit later, and shall use simpli ed versions of extraction tasks from these Conferences as examples throughout this paper. Figure 1 shows a simpli ed example from one of the earlier MUC's, involving terrorist events (MUC-3) [1]. For each terrorist event, the system had to determine the type of attack (bombing, arson, etc.), the date, location, perpetrator (if stated), targets, and e ects on targets. Other examples of extraction tasks are international joint ventures (where the arguments included the partners, the new venture, its product or service, etc.) and executive succession (indicating who was hired or red by which company for which position). Information extraction is a more limited task than \full text understanding". In full text understanding, we aspire to represent in a explicit fashion all the information in a text. In contrast, in information extraction we delimit in advance, as part of the speci cation of the task, the semantic range of the output: the relations we will represent, and the allowable llers in each slot of a relation.
Deployable Helical Antennas for CubeSats
(2013)

This paper explores the behavior of a self-deploying helical pantograph antenna for CubeSats. The helical pantograph concept is described along with concepts for attachment to the satellite bus. Finite element folding simulations of a pantograph consisting of eight helices are presented and compared to compaction force experiments done on a prototype antenna. Reflection coefficient tests are also presented, demonstrating the operating frequency range of the prototype antenna. The helical pantograph is shown to be a promising alternative to current small satellite antenna solutions.
Knowledge Extraction from Structured Engineering Drawings
(2008)

As a typical type of structured documents, table drawings are widely used in engineering fields. Knowledge extraction of such structured documents plays an important role in automatic interpretation systems. In this paper, we propose a new knowledge extraction method based on automatically analyzing drawing layout and extracting physical or logical structures from the given engineering table drawings. Then based on the automatic interpretation results, we further propose normalization method to integrate varied types of engineering tables with other engineering drawings and extract implied domain knowledge.